Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters. Sergey Ioffe, et al.,…

Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Sergey Ioffe, et al., "Batch Normalization: …

It also acts as a regularizer, in some cases eliminating the need for Dropout. Sergey Ioffe, et al., "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" https://arxiv.org/abs/1502.03167 内部共変量…

Batch Normalization allows us to use much higher learning rates and be less careful about initialization. Sergey Ioffe, et al., "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" https://arxiv.or…

Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Sergey Ioffe, et al., "Batch Normalization: Accelerating Deep Network Training by Reduc…